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Structural causes of citation gaps

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Abstract

The social identity of a researcher can affect their position in a community, as well as the uptake of their ideas. In many fields, members of underrepresented or minority groups are less likely to be cited, leading to citation gaps. Though this empirical phenomenon has been well-studied, empirical work generally does not provide insight into the causes of citation gaps. I will argue, using mathematical models, that citation gaps are likely due in part to the structure of academic communities. The existence of these ‘structural causes’ has implications for attempts to lessen citation gaps, and for proposals to make academic communities more efficient (e.g. by eliminating pre-publication peer review). These proposals have the potential to create feedback loops, amplifying current structural inequities.

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Notes

  1. There are a variety of reasons why we might not expect gender citation gaps in every field. See Sect. 3.4 for further discussion the factors relevant to the existence of citation gaps.

  2. In calling this a ‘structural cause’, I am using the phrase to indicate that the cause pertains to how the group is organized, or that the relations between the parts within the whole are important. The use of ‘structural’ in this phrase should not be taken to encompass all types of causes that are referred to as ‘structural’ (e.g. structural oppression or systemic discrimination), nor to preclude there being being a myriad of such structural causes present in the community. Thanks to a reviewer for pointing out this possible confusion.

  3. Building on Rubin and Schneider (2020), this paper can be thought of as demonstrating particular mechanisms regarding credit attribution within the ‘credit economy’ by which these inequities can emerge.

  4. A copy of the code for this paper has been made available on the Open Science Framework at https://osf.io/9suty/?view_only=70022ee795654ee9a22bc311a97a763e.

  5. The results presented here are for \(p=.3\), but similar results can be obtained with higher or lower probabilities.

  6. To get a reliable estimate of the expected citation gap, 100 different networks were formed for each combination of p(in) and majority group size, and 100 simulations of the citation process were run on each (with authors of the original papers chosen at random each time).

  7. In a model which excludes the two original authors from the accumulation of citations, results show that this decrease does not occur.

  8. For the results presented here, the likelihood to cite a paper based on a search, p is determined by the page, g, such that \(p= .95^{10g}\) and \(g = 10 - \frac{10c}{10+c}\), where c is the number of citation a paper has accumulated. Nothing depends on these particular equations, they merely capture the observation that more citations lead a paper to be on an earlier page, consequently making it more likely to be cited.

  9. One reason for this is that people are more likely to find a paper to cite through their network than by searching through pages of internet searches, so slightly more than 30% of citations come from looking through the network. For example, again looking at the extreme of high homophily and low representation, data from these simulations shows that around 40% of citations come from looking through the network. However, this cannot fully explain the results in Fig. 3. If it did, we would expect about 60% reduction in the majority’s advantage (from .34 to .14).

  10. Of course, minority group members may also have some chance of not citing other minority group members. This will affect overall citation rates in a similar way.

  11. The magnitude of the effect of bias or publication rates on the citation gap may be different, depending on the values those parameters take.

  12. Thanks to Chris Weaver and Riet Van Bork for discussions on this topic. See also Rubin and Schneider (2020) for a discussion of the role signaling can play in the context of assigning priority for scientific discoveries.

  13. Thanks to Mike Schneider for discussions on this point.

  14. See Bright (2017) for a decision theoretic model supporting this argument.

  15. Thanks to a reviewer for pushing me to clarify the points of agreement and disagreement.

  16. The method of forming these networks should not make a difference to the results, as long as we form a homophilic weighted directed network.

  17. Turn order each round is determined randomly, and the chance to publish an additional paper was set to 10% for the results below.

  18. The five papers were chosen by a weighted random sampling procedure. It is possible for a researcher to engage with a paper in multiple ways, e.g. by commenting on it and by sharing it with others.

  19. For the results presented here, this increase is .005, but the exact amount does not change the qualitative results. Additionally, weights were normalized at the start of the simulation and each time they evolve, so that the sum of each person’s outgoing arrows is one.

  20. Results are similar for other measures of centrality, e.g. closeness centrality, which is based on shortest path lengths between nodes.

  21. To get an estimate of how the process is expected to go, for each combination of parameters, 50 networks were formed randomly and five simulations were run on each of these networks. Data points in Fig. 5 represent each network that was formed, averaging over the five simulations. Results are very similar if instead each simulation is considered a data point.

  22. This is not to disparage those who make a concerted effort to be cognizant of citation gaps when compiling bibliographies, or journals which have made efforts to encourage authors to be cognizant. As emphasized, this paper does not deny the existence of individual level causes, such as implicit bias, which these efforts may counterbalance effectively.

  23. See Schneider et al. (2020) for an argument that exchange of ideas between social identity groups is epistemically important.

References

  • Adams, J., Brückner, H., & Naslund, C. (2019). Who counts as a notable sociologist on wikipedia? Gender, race, and the “professor test”. Socius,. https://doi.org/10.1177/2378023118823946.

    Article  Google Scholar 

  • Aksnes, D. W., Rorstad, K., Piro, F., & Sivertsen, G. (2011). Are female researchers less cited? A large-scale study of Norwegian scientists. Journal of the American Society for Information Science and Technology, 62(4), 628–636.

    Article  Google Scholar 

  • Beaudry, C., & Larivière, V. (2016). Which gender gap? Factors affecting researchers’ scientific impact in science and medicine. Research Policy, 45(9), 1790–1817.

    Article  Google Scholar 

  • Botts, T. F., Bright, L. K., Cherry, M., Mallarangeng, G., & Spencer, Q. (2014). What is the state of blacks in philosophy? Critical Philosophy of Race, 2(2), 224–242.

    Article  Google Scholar 

  • Bright, L. K. (2017). Decision theoretic model of the productivity gap. Erkenntnis, 82(2), 421–442.

    Article  Google Scholar 

  • Brown, N. E., & Samuels, D. (2018). Beyond the gender citation gap: Comments on dion, sumner, and mitchell. Political Analysis, 26(3), 328–330.

    Article  Google Scholar 

  • Bruner, J. P. (2019). Minority (dis) advantage in population games. Synthese, 196(1), 413–427.

    Article  Google Scholar 

  • Bruner, J. P., & O’Connor, C. (2015). Power, bargaining, and collaboration. In T. Boyer, C. Mayo-Wilson, & M. Weisberg (Eds.), Scientific collaboration and collective knowledge. Oxford: Oxford University Press.

    Google Scholar 

  • Cameron, E. Z., White, A. M., & Gray, M. E. (2016). Solving the productivity and impact puzzle: Do men outperform women, or are metrics biased? BioScience, 66(3), 245–252.

    Article  Google Scholar 

  • Corley, E. A., & Sabharwal, M. (2010). Scholarly collaboration and productivity patterns in public administration: Analysing recent trends. Public Administration, 88(3), 627–648.

    Article  Google Scholar 

  • Currarini, S., Jackson, M. O., & Pin, P. (2009). An economic model of friendship: Homophily, minorities, and segregation. Econometrica, 77(4), 1003–1045.

    Article  Google Scholar 

  • del Carmen, A., & Bing, R. L. (2000). Academic productivity of African Americans in criminology and criminal justice. Journal of Criminal Justice Education, 11(2), 237–249.

    Article  Google Scholar 

  • Di Vaio, G., Waldenström, D., & Weisdorf, J. (2012). Citation success: Evidence from economic history journal publications. Explorations in Economic History, 49(1), 92–104.

    Article  Google Scholar 

  • Dietrich, M. R., & Tambasco, B. H. (2007). Beyond the boss and the boys: Women and the division of labor in drosophila genetics in the united states, 1934–1970. Journal of the History of Biology, 40(3), 509–528.

    Article  Google Scholar 

  • Dion, M., & Mitchell, S. M. (2012). Gender, participation, and citations: Comparing peace science, political methodology, state politics, and IPE conferences. Poster presented at the annual conference of the Peace Science Society, Savannah, GA.

  • Dion, M. L., Sumner, J. L., & Mitchell, S. M. (2018). Gendered citation patterns across political science and social science methodology fields. Political Analysis, 26(3), 312–327.

    Article  Google Scholar 

  • Dworkin, J. D., Linn, K. A., Teich, E. G., Zurn, P., Shinohara, R. T., & Bassett, D. S. (2020). The extent and drivers of gender imbalance in neuroscience reference lists. Nature Neuroscience, 23(8), 918–926.

    Article  Google Scholar 

  • Eagly, A. H. (2020). Do the social roles that women and men occupy in science allow equal access to publication? Proceedings of the National Academy of Sciences, 117(11), 5553–5555.

    Article  Google Scholar 

  • Esarey, J., & Bryant, K. (2018). Are papers written by women authors cited less frequently? Political Analysis, 26(3), 331–334.

    Article  Google Scholar 

  • Ferber, M. A. (1986). Citations: Are they an objective measure of scholarly merit? Signs: Journal of Women in Culture and Society, 11(2), 381–389.

    Article  Google Scholar 

  • Ferber, M. A. (1988). Citations and networking. Gender & Society, 2(1), 82–89.

    Article  Google Scholar 

  • Ferber, M. A., & Brün, M. (2011). The gender gap in citations: Does it persist? Feminist Economics, 17(1), 151–158.

    Article  Google Scholar 

  • Golub, B., & Jackson, M. O. (2012). How homophily affects the speed of learning and best-response dynamics. The Quarterly Journal of Economics, 127(3), 1287–1338.

    Article  Google Scholar 

  • Håkanson, M. (2005). The impact of gender on citations: An analysis of college & research libraries, journal of academic librarianship, and library quarterly. College & Research Libraries, 66(4), 312–323.

    Article  Google Scholar 

  • Heesen, R., & Bright, L. K. (2019). Is peer review a good idea? The British Journal for the Philosophy of Science, 72(3), 635–663. https://doi.org/10.1093/bjps/axz029

  • Hesli, V. L., & Lee, J. M. (2011). Faculty research productivity: Why do some of our colleagues publish more than others? PS: Political Science & Politics, 44(2), 393–408.

    Google Scholar 

  • Hoffmann, C. P., Lutz, C., & Meckel, M. (2016). A relational altmetric? Network centrality on research G ate as an indicator of scientific impact. Journal of the Association for Information Science and Technology, 67(4), 765–775.

    Article  Google Scholar 

  • Hofstra, B., Kulkarni, V. V., Galvez, S.M.-N., He, B., Jurafsky, D., & McFarland, D. A. (2020). The diversity-innovation paradox in science. Proceedings of the National Academy of Sciences, 117(17), 9284–9291.

    Article  Google Scholar 

  • Huang, J., Gates, A. J., Sinatra, R., & Barabási, A.-L. (2020). Historical comparison of gender inequality in scientific careers across countries and disciplines. Proceedings of the National Academy of Sciences, 117(9), 4609–4616.

    Article  Google Scholar 

  • Knobloch-Westerwick, S., Glynn, C. J., & Huge, M. (2013). The matilda effect in science communication: An experiment on gender bias in publication quality perceptions and collaboration interest. Science Communication, 35(5), 603–625.

    Article  Google Scholar 

  • Kriegeskorte, N. (2012). Open evaluation: A vision for entirely transparent post-publication peer review and rating for science. Frontiers in Computational Neuroscience, 6, 79.

    Google Scholar 

  • Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Bibliometrics: Global gender disparities in science. Nature News, 504(7479), 211.

    Article  Google Scholar 

  • Leahey, E., Crockett, J. L., & Hunter, L. A. (2008). Gendered academic careers: Specializing for success? Social forces, 86(3), 1273–1309.

    Article  Google Scholar 

  • Lee, C. J. (2016). Revisiting current causes of women’s underrepresentation in science. In J. Saul & M. Brownstein (Eds.), Implicit bias and philosophy, Volume 1: Metaphysics and epistemology (pp. 265–282). Oxford, UK: Oxford University Press.

  • Lee, C. J., Sugimoto, C. R., Zhang, G., & Cronin, B. (2013). Bias in peer review. Journal of the American Society for Information Science and Technology, 64(1), 2–17.

    Article  Google Scholar 

  • Long, J. S. (1992). Measures of sex differences in scientific productivity. Social Forces, 71(1), 159–178.

    Article  Google Scholar 

  • Maliniak, D., Powers, R., & Walter, B. F. (2013). The gender citation gap in international relations. International Organization, 67(4), 889–922.

    Article  Google Scholar 

  • Martinez-Cola, M. (2020). Collectors, nightlights, and allies, oh my! White mentors in the academy. Understanding and Dismantling Privilege, 10(1), 25–57.

    Google Scholar 

  • Merritt, D. J. (2000). Scholarly influence in a diverse legal academy: Race, sex, and citation counts. The Journal of Legal Studies, 29(S1), 345–368.

    Article  Google Scholar 

  • Milkman, K. L., Akinola, M., & Chugh, D. (2015). What happens before? A field experiment exploring how pay and representation differentially shape bias on the pathway into organizations. Journal of Applied Psychology, 100(6), 1678.

    Article  Google Scholar 

  • Mitchell, S. D. (2009). Unsimple truths: Science, complexity, and policy. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Mitchell, S. M., Lange, S., & Brus, H. (2013). Gendered citation patterns in international relations journals. International Studies Perspectives, 14(4), 485–492.

    Article  Google Scholar 

  • Morgan, A. C., Economou, D. J., Way, S. F., & Clauset, A. (2018). Prestige drives epistemic inequality in the diffusion of scientific ideas. EPJ Data Science, 7(1), 40.

    Article  Google Scholar 

  • Nosek, B. A., & Bar-Anan, Y. (2012). Scientific utopia: I. Opening scientific communication. Psychological Inquiry, 23(3), 217–243.

    Article  Google Scholar 

  • Nosek, B. A., Graham, J., Lindner, N. M., Kesebir, S., Hawkins, C. B., Hahn, C., Schmidt, K., Motyl, M., Joy-Gaba, J., Frazier, R., et al. (2010). Cumulative and career-stage citation impact of social-personality psychology programs and their members. Personality and Social Psychology Bulletin, 36(10), 1283–1300.

    Article  Google Scholar 

  • O’Connor, C. (2019). The origins of unfairness: Social categories and cultural evolution. Oxford: Oxford University Press.

    Book  Google Scholar 

  • O’Connor, C., Bright, L. K., & Bruner, J. P. (2019). The emergence of intersectional disadvantage. Social Epistemology, 33(1), 23–41.

    Article  Google Scholar 

  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Broadway Books.

    Google Scholar 

  • Østby, G., Strand, H., Nordås, R., & Gleditsch, N. P. (2013). Gender gap or gender bias in peace research? Publication patterns and citation rates for journal of peace research, 1983–2008. International Studies Perspectives, 14(4), 493–506.

    Article  Google Scholar 

  • Powell, A., Hassan, T. M., Dainty, A. R., & Carter, C. (2009). Note: Exploring gender differences in construction research: A European perspective. Construction Management and Economics, 27(9), 803–807.

    Article  Google Scholar 

  • Rubin, H., & O’Connor, C. (2018). Discrimination and collaboration in science. Philosophy of Science, 85(3), 380–402.

    Article  Google Scholar 

  • Rubin, H., & Schneider, M. D. (2020). Priority and privilege in scientific discovery. Studies in History and Philosophy of Science Part A, 89, 202--211.

  • Sarsons, H. (2017). Recognition for group work: Gender differences in academia. American Economic Review, 107(5), 141–45.

    Article  Google Scholar 

  • Schneider, M. D., Rubin, H., & O’Connor, C. (2020). Promoting diverse collaborations. In G. Ramsey & A. De Block (Eds.), The dynamics of science: Computational frontiers in history and philosophy of science. Pittsburgh: Pittsburgh University Press.

    Google Scholar 

  • Sen, M. (2018). Response to dion, sumner, and mitchell. Political Analysis, 26(3), 335–337.

    Article  Google Scholar 

  • Slyder, J. B., Stein, B. R., Sams, B. S., Walker, D. M., Jacob Beale, B., Feldhaus, J. . J. ., & Copenheaver, C. A. (2011). Citation pattern and lifespan: A comparison of discipline, institution, and individual. Scientometrics, 89(3), 955–966.

    Article  Google Scholar 

  • Stack, S. (2002). Gender and scholarly productivity: The case of criminal justice. Journal of Criminal Justice, 30(3), 175–182.

    Article  Google Scholar 

  • Steinpreis, R. E., Anders, K. A., & Ritzke, D. (1999). The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex Roles, 41(7–8), 509–528.

    Article  Google Scholar 

  • Teixeira da Silva, J. A., & Dobránszki, J. (2015). Problems with traditional science publishing and finding a wider niche for post-publication peer review. Accountability in Research, 22(1), 22–40.

    Article  Google Scholar 

  • Vale, R. D. (2015). Accelerating scientific publication in biology. Proceedings of the National Academy of Sciences, 112(44), 13439–13446.

    Article  Google Scholar 

  • Wang, Y. S., Lee, C. J., West, J. D., Bergstrom, C. T. & Erosheva, E. A. (2019). Gender-based homophily in collaborations across a heterogeneous scholarly landscape. 1–33.

  • Ward, K. B., Gast, J., & Grant, L. (1992). Visibility and dissemination of women’s and men’s sociological scholarship. Social Problems, 39(3), 291–298.

    Article  Google Scholar 

  • West, J. D., & Bergstrom, C. T. (2021). Misinformation in and about science. Proceedings of the National Academy of Sciences, 118(15), 1–8.

  • West, J. D., Jacquet, J., King, M. M., Correll, S. J., & Bergstrom, C. T. (2013). The role of gender in scholarly authorship. PloS One, 8(7), e66212.

    Article  Google Scholar 

  • Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.

    Article  Google Scholar 

  • Yang, Y., Chawla, N. V., & Uzzi, B. (2019). A network’s gender composition and communication pattern predict women’ leadership success. Proceedings of the National Academy of Sciences, 116(6), 2033–2038.

    Article  Google Scholar 

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Acknowledgements

Many thanks to Mike Schneider for ideas, discussions, criticisms, and so on. The initial models here are similar to those developed in a paper co-authored with Mike (Priority and Privilege in Scientific Discovery). Thanks to Justin Bruner, the Spring 2021 Fellows at the Pitt Center for Philosophy of Science, and two reviewers for comments on the paper. Also, thanks to audiences at University of Groningen, the Women in Academia conference at UC Irvine, and the Pitt Center for Philosophy of Science lunchtime talk for feedback. This material is based upon work supported by the National Science Foundation under Grant No. 2045007.

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    Hannah Rubin

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Rubin, H. Structural causes of citation gaps. Philos Stud 179, 2323–2345 (2022). https://doi.org/10.1007/s11098-021-01765-3

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